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Multi-omics analysis identified serum B4GALT1 as a prognostic factor for small cell lung cancer

J Thorac Dis. 2026 Apr 30;18(4):353. doi: 10.21037/jtd-2025-1-2610. Epub 2026 Mar 20.

ABSTRACT

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor characterized by rapid progression, early metastasis, and high mortality, with limited effective long-term treatment options. B4GALT1, a Ξ²-1,4-galactosyltransferase, has been implicated in the malignant progression of various cancers, but its specific role and underlying mechanisms in SCLC remain largely unexplored. We conducted a multi-omics analysis and clinical sample study to explore the function of B4GALT1 in SCLC.

METHODS: This study comprehensively investigated the expression pattern, functional significance, and clinical relevance of B4GALT1 in SCLC. We conducted multi-omics analyses, including single-cell data processing, InferCNV analysis, and immune infiltration analysis, to explore the association between B4GALT1 and the immune microenvironment of SCLC and patient survival. To determine B4GALT1 as a potential circulating biomarker, quantitative data-independent acquisition (DIA) proteomics analysis was performed on serum samples from SCLC patients and healthy controls. Enzyme-linked immunosorbent assay (ELISA) was used to further verify the differential expression of serum B4GALT1 in a larger cohort of SCLC patients, to evaluate its diagnostic, prognostic, and treatment response predictive value.

RESULTS: Multi-omics analysis revealed that B4GALT1 expression was significantly associated with patient survival. The expression of B4GALT1 positively correlated with macrophage infiltration in the tumor and negatively correlated with CD4+ T cells in the tumor. There was a negative correlation in inactivated naΓ―ve B cells, eosinophils, and CD4 naΓ―ve T cells, while it showed a positive correlation in dendritic cells, M0/M1/M2 macrophages, natural killer (NK) cells, CD8 T cells, follicular helper T cells, and regulatory T cells. ELISA results showed that serum protein B4GALT1 expression was higher in patients with SCLC than in healthy controls. Elevated serum B4GALT1 protein levels correlated with poor treatment outcomes in patients with SCLC undergoing chemoradiotherapy.

CONCLUSIONS: Our findings establish B4GALT1 as a critical prognostic, diagnostic, and predictive biomarker in SCLC, with its expression closely linked to the tumor immune microenvironment and treatment response. Targeting B4GALT1 or its related pathways may represent a novel therapeutic strategy, and serum B4GALT1 holds promise as a liquid biopsy marker for SCLC patient stratification, monitoring, and guiding treatment decisions.

PMID:42182735 | PMC:PMC13190155 | DOI:10.21037/jtd-2025-1-2610

Multi-omics analysis identified serum B4GALT1 as a prognostic factor for small cell lung cancer

25 May 2026 at 18:00

J Thorac Dis. 2026 Apr 30;18(4):353. doi: 10.21037/jtd-2025-1-2610. Epub 2026 Mar 20.

ABSTRACT

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor characterized by rapid progression, early metastasis, and high mortality, with limited effective long-term treatment options. B4GALT1, a Ξ²-1,4-galactosyltransferase, has been implicated in the malignant progression of various cancers, but its specific role and underlying mechanisms in SCLC remain largely unexplored. We conducted a multi-omics analysis and clinical sample study to explore the function of B4GALT1 in SCLC.

METHODS: This study comprehensively investigated the expression pattern, functional significance, and clinical relevance of B4GALT1 in SCLC. We conducted multi-omics analyses, including single-cell data processing, InferCNV analysis, and immune infiltration analysis, to explore the association between B4GALT1 and the immune microenvironment of SCLC and patient survival. To determine B4GALT1 as a potential circulating biomarker, quantitative data-independent acquisition (DIA) proteomics analysis was performed on serum samples from SCLC patients and healthy controls. Enzyme-linked immunosorbent assay (ELISA) was used to further verify the differential expression of serum B4GALT1 in a larger cohort of SCLC patients, to evaluate its diagnostic, prognostic, and treatment response predictive value.

RESULTS: Multi-omics analysis revealed that B4GALT1 expression was significantly associated with patient survival. The expression of B4GALT1 positively correlated with macrophage infiltration in the tumor and negatively correlated with CD4+ T cells in the tumor. There was a negative correlation in inactivated naΓ―ve B cells, eosinophils, and CD4 naΓ―ve T cells, while it showed a positive correlation in dendritic cells, M0/M1/M2 macrophages, natural killer (NK) cells, CD8 T cells, follicular helper T cells, and regulatory T cells. ELISA results showed that serum protein B4GALT1 expression was higher in patients with SCLC than in healthy controls. Elevated serum B4GALT1 protein levels correlated with poor treatment outcomes in patients with SCLC undergoing chemoradiotherapy.

CONCLUSIONS: Our findings establish B4GALT1 as a critical prognostic, diagnostic, and predictive biomarker in SCLC, with its expression closely linked to the tumor immune microenvironment and treatment response. Targeting B4GALT1 or its related pathways may represent a novel therapeutic strategy, and serum B4GALT1 holds promise as a liquid biopsy marker for SCLC patient stratification, monitoring, and guiding treatment decisions.

PMID:42182735 | PMC:PMC13190155 | DOI:10.21037/jtd-2025-1-2610

Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning

arXiv:2601.11109v3 Announce Type: replace-cross Abstract: Vision-as-inverse-graphics, the concept of reconstructing images into editable programs, remains challenging for Vision-Language Models (VLMs), which inherently lack fine-grained spatial grounding in one-shot settings. To address this, we introduce VIGA (Vision-as-Inverse-Graphics Agent), an interleaved multimodal reasoning framework where symbolic logic and visual perception actively cross-verify each other. VIGA operates through a tightly coupled code-render-inspect loop: synthesizing symbolic programs, projecting them into visual states, and inspecting discrepancies to guide iterative edits. Equipped with high-level semantic skills and an evolving multimodal memory, VIGA sustains evidence-based modifications over long horizons. This training-free, task-agnostic framework seamlessly supports 2D document generation, 3D reconstruction, multi-step 3D editing, and 4D physical interaction. Finally, we introduce BlenderBench, a challenging visual-to-code benchmark. Empirically, VIGA substantially improves accuracy compared with one-shot baselines in BlenderGym (35.32%), SlideBench (117.17%) and our proposed BlenderBench (124.70%).
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